Large scale egomotion and error analysis with visual features

M. Cazorla, D. Viejo, A. Hernandez, J. Nieto, E. Nebot

Resultado de la investigación

2 Citas (Scopus)

Resumen

Several works deal with 3D data in SLAM problem but many of them are focused on short scale maps. In this paper, we propose a method that can be used for computing the 6DoF trajectory performed by a robot from the stereo images captured during a large scale trajectory. The method transforms robust 2D features extracted from the reference stereo images to the 3D space. These 3D features are then used for obtaining the correct robot movement. Both Sift and Surf methods for feature extraction have been used. Also, a comparison between our method and the results of the ICP algorithm have been performed. We have also made a study about errors in stereo cameras.

Idioma originalEnglish
Páginas (desde-hasta)19-24
Número de páginas6
PublicaciónJournal of Physical Agents
Volumen4
N.º1
EstadoPublished - 7 sep 2010
Publicado de forma externa

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering

Huella Profundice en los temas de investigación de 'Large scale egomotion and error analysis with visual features'. En conjunto forman una huella única.

  • Citar esto

    Cazorla, M., Viejo, D., Hernandez, A., Nieto, J., & Nebot, E. (2010). Large scale egomotion and error analysis with visual features. Journal of Physical Agents, 4(1), 19-24.